{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n",
    "import matplotlib.cbook as cbook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the matched ERAI data from the disaster locations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "floods = pd.read_pickle('matched_disaster_metdata/CDHR_Floodlocations_2006_2015_metdata.pkl')\n",
    "storms = pd.read_pickle('matched_disaster_metdata/CDHR_Stormlocations_2006_2015_metdata.pkl')\n",
    "droughts = pd.read_pickle('matched_disaster_metdata/CDHR_Droughtlocations_2006_2018_metdata.pkl')\n",
    "heatwaves = pd.read_pickle('matched_disaster_metdata/CDHR_Heatwavelocations_2006_2018_metdata.pkl')\n",
    "coldwaves = pd.read_pickle('matched_disaster_metdata/CDHR_Coldwavelocations_2006_2018_metdata.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the ERAI distributions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "tp180 = pd.read_pickle('ERAI_distributions/tp180_dist.pkl') # 180-day (6-month) accumulated precipitation\n",
    "winds = pd.read_pickle('ERAI_distributions/windspeed_dist.pkl') # maximum wind speed at 10m height\n",
    "tmax = pd.read_pickle('ERAI_distributions/tmax_dist.pkl') # daily maximum temperature at 2m height\n",
    "tmin = pd.read_pickle('ERAI_distributions/tmin_dist.pkl') # daily maximum temperature at 2m height\n",
    "precip = pd.read_pickle('ERAI_distributions/tp1_dist.pkl') # daily accumulated precipitation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use Welch's t-test between the two distributions (uncomment the lines to take random subsets of the entire distribution):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Storms:\n",
      "Ttest_indResult(statistic=49.532681418721396, pvalue=0.0)\n",
      "Floods:\n",
      "Ttest_indResult(statistic=37.59306649556825, pvalue=3.936560158733595e-280)\n",
      "Droughts:\n",
      "Ttest_indResult(statistic=6.165485405037557, pvalue=2.8984472009352122e-08)\n",
      "Heat waves:\n",
      "Ttest_indResult(statistic=17.929150830516168, pvalue=1.0662522162928524e-48)\n",
      "Cold waves:\n",
      "Ttest_indResult(statistic=2.3118792960875454, pvalue=0.021016008610964976)\n"
     ]
    }
   ],
   "source": [
    "winds_sample = winds['U10']\n",
    "# winds_sample = np.random.choice(winds_sample,size=len(storms['U10'])) # test against a random subset\n",
    "stormt = stats.ttest_ind(storms['U10'],winds_sample,equal_var=False)\n",
    "print('Storms:')\n",
    "print(stormt)\n",
    "\n",
    "tp_sample = precip['tp1']\n",
    "# tp_sample = np.random.choice(tp_sample,size=len(floods['tp1'])) # test against a random subset\n",
    "floodt = stats.ttest_ind(floods['tp1'],tp_sample,equal_var=False)\n",
    "print('Floods:')\n",
    "print(floodt)\n",
    "\n",
    "tp180_sample = tp180['tp180']\n",
    "# tp180_sample = np.random.choice(tp180_sample,size=len(droughts['tp180'])) # test against a random subset\n",
    "droughtt = stats.ttest_ind(droughts['tp180'],tp180_sample,equal_var=False)\n",
    "print('Droughts:')\n",
    "print(droughtt)\n",
    "\n",
    "tmax_sample = tmax['tmax']\n",
    "# tmax_sample = np.random.choice(tmax['tmax'],size=len(heatwaves['Tmax'])) # test against a random subset\n",
    "heatt = stats.ttest_ind(heatwaves['Tmax'],tmax_sample,equal_var=False)\n",
    "print('Heat waves:')\n",
    "print(heatt)\n",
    "\n",
    "tmin_sample = tmin['tmin']\n",
    "# tmin_sample = np.random.choice(tmin_sample,size=len(coldwaves['Tmin'])) # test against a random subset\n",
    "coldt = stats.ttest_ind(coldwaves['Tmin'],tmin_sample,equal_var=False)\n",
    "print('Cold waves:')\n",
    "print(coldt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.rcParams.update({'font.size': 16,\n",
    "                     'axes.linewidth':2,\n",
    "                     'xtick.major.size':10,\n",
    "                     'ytick.major.size':10,\n",
    "                     'xtick.major.width':2,\n",
    "                     'ytick.major.width':2})\n",
    "\n",
    "meandict = dict(marker='+',mew=2.5,markersize=10,markeredgecolor='firebrick')\n",
    "mediandict = dict(linewidth=0)\n",
    "whiskerdict = dict(linewidth=2)\n",
    "capdict = dict(linewidth=2)\n",
    "boxdict = dict(linewidth=2)\n",
    "\n",
    "data1 = list([winds['U10'],storms['U10']])\n",
    "labels1 = list(['All','Storms'])\n",
    "data2 = list([precip['tp1'],floods['tp1']])\n",
    "labels2 = list(['All','Floods'])\n",
    "data3 = list([droughts['tp180'],tp180['tp180']])\n",
    "labels3 = list(['All','Droughts'])\n",
    "data4 = list([tmax['tmax'],heatwaves['Tmax']])\n",
    "labels4 = list(['All','Heat waves'])\n",
    "data5 = list([tmin['tmin'],coldwaves['Tmin']])\n",
    "labels5 = list(['All','Cold waves'])\n",
    "\n",
    "stormlims = [0,15]\n",
    "floodlims = [0,25]\n",
    "droughtlims = [0,1500]\n",
    "heatlims = [-40,80]\n",
    "coldlims = [-45,45]\n",
    "ylims = list([stormlims,floodlims,droughtlims,heatlims,coldlims])\n",
    "alldata = list([data1,data2,data3,data4,data5])\n",
    "alllabels = list([labels1,labels2,labels3,labels4,labels5])\n",
    "subtitles = list(['a) t = %.2f**'%(np.round(stormt[0],2)),\n",
    "                  'b) t = %.2f**'%(np.round(floodt[0],2)),\n",
    "                  'c) t = %.2f**'%(np.round(droughtt[0],2)),\n",
    "                  'd) t = %.2f**'%(np.round(heatt[0],2)),\n",
    "                  'e) t = %.2f*'%(np.round(coldt[0],2))])\n",
    "ylabels = list(['$U_{max}$ [m/s]',\n",
    "                '$P_{1d}$ [mm]',\n",
    "                '$P_{180d}$ [mm]',\n",
    "                '$T_{max}$ [° C]',\n",
    "                '$T_{min}$ [° C]'])\n",
    "\n",
    "fig,axes = plt.subplots(ncols=5,nrows=1,figsize=(18,5))\n",
    "\n",
    "for x in range(5):\n",
    "    \n",
    "    bxpstats = list()\n",
    "    for dataset, label in zip(alldata[x],alllabels[x]):\n",
    "        bxpstats.extend(cbook.boxplot_stats(np.ravel(dataset), labels=[label]))\n",
    "    axes[x].bxp(bxpstats,shownotches=True,widths=0.4,vert=True,\n",
    "                boxprops=boxdict,\n",
    "                capprops=capdict,\n",
    "                whiskerprops=whiskerdict,\n",
    "                showfliers=False,\n",
    "                showmeans=True,\n",
    "                medianprops=mediandict,\n",
    "                flierprops=dict(marker='.',markersize=2),\n",
    "                meanprops=meandict)\n",
    "    axes[x].set(ylim=ylims[x],xticklabels=alllabels[x])\n",
    "    axes[x].set_ylabel(ylabels[x])\n",
    "    axes[x].tick_params(width=2)\n",
    "    axes[x].spines['top'].set_visible(False)\n",
    "    axes[x].spines['right'].set_visible(False)\n",
    "    axes[x].set_title(subtitles[x],pad=25)\n",
    "\n",
    "axes[0].set_yticks(np.arange(0,16,2.5))\n",
    "axes[2].set_yticks(np.arange(0,1501,250))\n",
    "axes[4].set_yticks(np.arange(-45,46,15))\n",
    "fig.tight_layout()\n",
    "fig.patch.set_facecolor('white')\n",
    "\n",
    "## Save plot\n",
    "# fig.savefig('validation_distr.png',bbox_inches='tight',dpi=300)\n",
    "# fig.savefig('validation_distr.pdf',bbox_inches='tight')"
   ]
  }
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